Parallel Computer Workload Modeling with Markov Chains

Abstract

In order to evaluate di#erent scheduling strategies for parallel computers, simulations are often executed. As the scheduling quality highly depends on the workload that is served on the parallel machine, a representative workload model is required. Common approaches such as using a probability distribution model can capture the static feature of real workloads, but they do not consider the temporal relation in the traces. In this paper, a workload model is presented which uses Markov chains for modeling job parameters. In order to consider the interdependence of individual parameters without requiring large scale Markov chains, a novel method for transforming the states in di#erent Markov chains is presented. The results show that the model yields closer results to the real workloads than other common approaches